Post-training pruning compresses Large Language Models (LLMs) without full-model retraining, but performance degrades sharply at high sparsity. We identify two limitations in existing methods: local importance estimates miss global spectral structure, and uniform within-block sparsity ignores heterogeneous sensitivity across transformer projections. We propose PRISM, a post-training pruning framework with training-free mask construction and optional fixed-mask reconstruction optimization. PRISM augments activation-based scoring with two adaptive-rank SVD-derived signals for principal-subspace and residual-aware importance, combines the factors through parameter-free CV-based fusion, and allocates sparsity non-uniformly across attention and MLP projections using a gradient-weighted matrix-level sensitivity proxy. Across six Qwen3 and LLaMA models, PRISM consistently outperforms existing pruning baselines, yielding up to 16.8% relative improvement in WikiText-2 perplexity under the evaluated sparsity settings.