A new framework uses symmetric human-LLM Q-sorts to evaluate how large language models structurally align with moral values. By comparing rankings of 140 moral statements across 12 LLMs and a human reference sample, the study identifies cross-family heterogeneity and localized misalignments, showing that global performance scores can mask structural flaws. The results highlight the need for structural evaluations to complement traditional item-level moral benchmarks.