Subgraph matching is a core task in graph analytics, widely used in domains such as biology, finance, and social networks. Existing top $k$ diversified methods typically focus on maximizing vertex coverage, but often return results in the same region, limiting topological diversity. We propose the Distance-Diversified Top-$k$ Subgraph Matching (DT$k$SM) problem, which selects $k$ isomorphic matches with maximal pairwise topological distances to better capture global graph structure. To address its computational challenges, we introduce the Partition based Distance Diversity (PDD) framework, which partitions the graph and retrieves diverse matches from distant regions. To further enhance efficiency, we develop two optimizations: embedding-driven partition filtering and densest-based partition selection over a Partition Adjacency Graph. Experiments on 12 real world datasets show our approach achieves up to four orders of magnitude speedup over baselines, with 95\% of results reaching 80\% of optimal distance diversity and 100\% coverage diversity.