Large language models make natural-language-to-MILP modeling increasingly plausible, but reliably selecting among multiple generated programs remains challenging: structurally similar errors can create false majorities, while solving every candidate integer program is costly. We propose Structural-Semantic Consistency (SSC), a best-of-K selector combining structural consistency, static quality, text alignment, and two diagnostics obtained from continuous LP relaxations. Notably, SSC does not require solving any candidate integer programs during selection. Across seven benchmarks and 618 fixed candidate pools, SSC obtains 55.7% accuracy, compared with a random baseline of 51.5%, 53.7% for a zero-shot listwise LLM judge, and 55.3% for a leave-one-benchmark-out learned ranker. The symbolic fusion reaches 53.6%, with LP diagnostics adding 2.1 percentage points. These results establish multi-signal weak verification as a competitive and interpretable baseline for candidate selection.