Multimodal recommendation systems increasingly adopt GNNs to perform message passing on the user–item interaction graph and content-based item graphs to learn user and item representations. However, existing methods that use static-graph GNNs in multimodal recommendation face two key limitations: (i) representation homogenization, where repeated aggregation on a fixed topology makes node embeddings overly similar and weakens personalized signals; and (ii) incomplete context modeling, where similarity-based item graphs mainly capture local pairwise resemblance but miss group- or scenario-level relations that are far apart in the similarity space. Together, these limitations reduce the quality of learned node representations on static graphs and lead to suboptimal recommendations. To alleviate these limitations, we propose ESG-Rec, a unified multi-view framework that constructs three complementary structural views: degree-aware structural diversification to preserve individualized collaborative signals, content-based semantic modeling to provide stable local relations, and group-level semantic modeling to capture higher-order contextual information. ESG-Rec further performs multi-view contrastive alignment to learn consistent representations across views. Extensive experiments on benchmark datasets demonstrate that ESG-Rec consistently improves recommendation performance over strong multimodal baselines and effectively mitigates the above two limitations.