Molecular graph representation learning has made important contributions in the field of drug discovery and design. Most of the existing works leverages graph neural networks (GNNs) as a backbone for encoding implicit molecular representations, which are honed through various self-supervised learning (SSL) pretext tasks. However, vanilla GNN encoders ignore the implicit chemical structure information and functions in molecular motifs, and existing works do not adequately capture various modalities such as attributes, semantics, and structures in molecules and motifs. Combining the various information mentioned above is still challenging. To address the above issues, we introduce property-aware motifs to both node-level and graph-level pre-training tasks. First, we propose a new node-level pre-training task MotifMask (MOM) which masks molecules by atom type to capture semantics in the motif range, and alleviates the negative migration problem of AttrMask. Further, we propose Three-Layer Augmented Graph Contrastive Learning (TECL) to comprehensively capture the structural information of different layers of the molecule. Finally, the complementary strengths of the semantic node-level task and the structural graph-level task are combined to form the MOMTERL framework. MOMTERL, through its design of local-global tasks, demonstrates better performance in molecular property prediction by effectively integrating different information.