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Third-party applications (or commonly referred to the apps) proliferate on the web and mobile platforms in recent years. The tremendous amount of available apps in app market-places suggests the necessity of designing effective app search engines. However, existing app search engines typically ignore the latent semantics in the app corpus and thus usually fail to provide high-quality app snippets and effective app rankings. In this paper, we present a novel framework named Panorama to provide independent search results for Android apps with semantic awareness. We first propose the App Topic Model (ATM) to discover the latent semantics from the app corpus. Based on the discovered semantics, we tackle two central challenges that are faced by current app search engines: (1) how to generate concise and informative snippets for apps and (2) how to rank apps effectively with respect to search queries. To handle the first challenge, we propose several new metrics for measuring the quality of the sentences in app description and develop a greedy algorithm with fixed probability guarantee of near-optimal performance for app snippet generation. To handle the second challenge, we propose a variety of new features for app ranking and also design a new type of inverted index to support efficient Top-k app retrieval. We conduct extensive experiments on a large-scale data collection of Android apps and build an app search engine prototype for human-based performance evaluation. The proposed framework demonstrates superior performance against several strong baselines with respect to different metrics.