This chapter provides a comprehensive overview of the recent advances in the computational design of molecular crystals, focusing on the results of the 7th blind test for crystal structure prediction. This blind test presented more challenging tasks, including more complex molecular systems and multi-component crystals. The evaluation, divided into structure generation and structure ranking phases, highlighted the progress of the dispersion-corrected density functional theory (DFT-D) method and the effectiveness of its machine learning potentials. In particular, accuracy comparable to the DFT-D method was achieved at significantly lower computational cost, and disordered structures were successfully predicted for the first time. Major methodological advances include optimization of the hierarchical structure evaluation procedure, consideration of thermodynamic effects (phonon and free energy calculations), and integration with experimental data (e.g., powder X-ray diffraction data). However, several key challenges remain, including balancing computational cost and accuracy, handling of disordered structures and entropy, incorporating crystallization kinetics, and improving structure identification methods. Future directions include further development and application of machine learning and artificial intelligence, integration of multiscale modeling, seamless linkage with experiments, rational design of functional materials, and exploration of quantum computing. Advances in this area have the potential to revolutionize the development of new functional materials and pharmaceuticals, and close collaboration among computational scientists, experimental scientists, and industry is essential.