Fault localization is one of the most challenging and time-consuming tasks in software debugging. Traditional methods, such as Spectrum-Based Fault Localization (SBFL) and Mutation-Based Fault Localization (MBFL), rely on coverage or mutation outcomes to rank suspicious program elements. However, these methods are limited by their reliance on a single type of information, which often fails to capture the full complexity of fault propagation. In particular, they fail to fully utilize structural semantics, especially contextual relationships among functions, which are key to understanding fault propagation. To address these limitations, we propose EMS-HFL (Execution-Mutation-Structural Hybrid Fault Localization), a method that systematically synergy three categories of information: execution behavior (i.e., coverage data), mutation behavior (i.e., mutant outcomes), and structural semantics (i.e., function call chains). Coverage data and mutant outcomes contribute basic fault information, while function call chains capture the broader execution context that helps better measure the suspiciousness of methods. Empirical evaluations on 262 faulty versions from the Defects4J benchmark show that EMS-HFL successfully localizes 90 faults at the Top-1 position, outperforming SBFL, MBFL, and other advanced hybrid approaches. Ablation studies confirm that each information source (coverage, mutation, and structural semantics) is crucial, with their combination significantly boosting performance.