Multi-manipulator systems are essential in warehouse automation, assembly, manufacturing, and logistics, requiring robots to coordinate within shared workspaces while avoiding collisions. This coordination is often formulated as multi-manipulator motion planning, which is notoriously hard to solve due to the high dimensionality of the configuration space. To this end, decoupled approaches are often used, such as prioritized planning. In the latter approach, robots are assigned priorities and plan trajectories sequentially, treating higher-priority agents as dynamic obstacles. Thus, the performance of a prioritized planner critically depends on the underlying single-agent planner. Recent advanced algorithms in this area, tailored specifically for manipulation planning in the presence of moving obstacles (high-priority robots), include Space-Time RRT* (ST-RRT*), which enhances RRTConnect for the time dimension, and Safe-Interval RRT (SI-RRT), which enriches RRTConnect with safe interval planning. While prior research demonstrated that SI-RRT outperforms ST-RRT* in the single-agent scenarios, their effectiveness in the multi-robot settings remains unexplored. This paper presents the first comprehensive evaluation of ST-RRT* and SI-RRT within prioritized planning for multi-manipulator systems. We compare both planners using the TAPAS dataset (containing diverse pick-and-place tasks) and synthetic random tests, scaling from 2 to 8 robots. On synthetic tests, SI-RRT achieves lower path lengths and makespan despite increased planning time for 6–8 robots due to constrained time corridors, caused by raised scene complexity resulting from reduced makespan. On the TAPAS benchmark, SI-RRT consistently demonstrates the same faster computation, shorter trajectories, better success rate and reduced makespan across all tested scenarios. Code for empirical evaluation is publicly available at github.com/PathPlanning/ManipulationPlanning-SI-RRT/tree/Multiagent-comp